AI-Driven eSIM Profile Selection in Heterogeneous Platforms
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Solution Overview
Problem
The transition from x86 to ARM-based processors in Information Handling Systems (IHSs) presents challenges in managing embedded Subscriber Identity Module (eSIM) credentials due to differences in operating systems and hardware architectures, requiring new management, customization, and optimization strategies.
Innovation Solution
A heterogeneous computing platform with an orchestrator device that deploys AI models to select and manage eSIM profiles based on context and telemetry data without host OS involvement, using devices like SoC, FPGA, or ASIC, and components like EC or BMC to authenticate credentials and adjust wireless subscriptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI models are deployed to manage eSIM credentials dynamically, then adaptability and optimization of wireless connectivity is improved, but device complexity and computational resource requirements increase
Solution Approach 1:
The system segments the eSIM credential management function by deploying AI models on specific dedicated devices within the heterogeneous computing platform rather than requiring all devices to perform the full management function. This allows complex AI processing to be isolated to particular components while simpler devices handle only credential storage and communication.
Solution Approach 2:
The heterogeneous computing platform is designed with multi-functional devices that can serve multiple purposes: some devices handle AI model execution for credential selection, while others handle wireless communication and credential storage. This universal design allows the system to manage eSIM credentials adaptively across different device types without requiring each device to be specialized.
2Reliability
If firmware services are used to collect context and telemetry data without host OS involvement, then system reliability and data collection efficiency is improved, but device complexity increases
Solution Approach 1:
The patent extracts the data collection function from the host operating system and implements it directly in firmware services at a lower level. This extraction ensures that data collection occurs independently of OS operations, improving reliability by eliminating OS-level failures as a point of failure. The firmware services directly access hardware sensors and collect telemetry data without requiring OS mediation.
Solution Approach 2:
The firmware services act as intermediaries between the hardware sensors and the AI models. These services collect context and telemetry data from various hardware components and make this data available to AI models for credential selection, enabling reliable data collection while isolating the complexity of hardware interfaces from the higher-level AI processing.
3Productivity
If multiple devices are deployed in heterogeneous computing platform, then functionality and processing capability is improved, but management complexity and system configuration difficulty increases
Solution Approach 1:
The patent merges the management functions across multiple heterogeneous devices by implementing a unified firmware service architecture that operates independently of the host OS. This consolidation allows different device types (AI processing devices, wireless communication devices, storage devices) to work together seamlessly through common firmware interfaces, reducing management complexity despite the diversity of individual components.
Data Source
AI summary
Systems and methods for managing embedded Subscriber Identity Module (eSIM) credentials in heterogenous computing platforms. In a non-limiting embodiment, an Information Handling System may include: a heterogeneous computing platform having a plurality of devices and a memory coupled to the platform, where the memory includes firmware instructions that, upon execution by a respective device among the plurality of devices, enable a respective device to provide a corresponding firmware service, and at least one of the devices operates as an orchestrator configured to: deploy or instruct another device to deploy an AI model configured to produce an inference result based, at least in part, upon context or telemetry data received from a subset of the devices; select an eSIM profile based upon the inference result; and transmit a credential within the selected eSIM profile to a wireless controller configured to authenticate the credential.